For Project 1 the answer to each question should include a chart and a written response. The years labels on your charts should not include a comma. At least two of your charts must include reference marks.
How does your name at your birth year compare to its use historically?
So, fun fact, the name “Wilkin” doesn’t even show up in the national dataset. It’s that rare. I guess I’m too unique for the U.S. government to keep track of. Since my real name was ghosting the data, I rolled with “William” instead it’s close enough, and it actually shows up. While Wilkin is off being mysterious, William gives us a look at how common names behave over time, unlike mine, which apparently slipped past every hospital form in America.
Show the code
wilkin_df = df[df['name'] =='William']highlight_point = wilkin_df[wilkin_df['year'] ==1999]plot = ggplot(wilkin_df, aes(x='year', y='n')) +\ geom_line(color='#1f77b4', size=1.2) +\ ggtitle("Popularity of the Name 'William' Over Time") +\ xlab("Year") + ylab("Number of Babies Named Wilkin") +\ scale_x_continuous(breaks=list(range(1880, 2021, 20))) +\ theme_minimal()plot + geom_point(aes(x='year', y='n'), data=highlight_point, color='red', size=4)
QUESTION|TASK 2
If you talked to someone named Brittany on the phone, what is your guess of his or her age? What ages would you not guess?
Brittany was an extremely popular name from the mid-1980s to the early 1990s. If I met someone named Brittany, I would guess they were born between 1985 and 1995, which would make them between 30 and 40 years old in 2025. I would not guess that a Brittany is younger than 20 or older than 50, as the name has sharply declined in usage since its peak.
Show the code
brittany_df = df[df['name'] =='Brittany']ggplot() +\ geom_line(data=brittany_df, mapping=aes(x='year', y='n'), color='#800080', size=1.2) +\ geom_vline(xintercept=1990, color='red', linetype='dashed') +\ ggtitle("Popularity of the Name 'Brittany' Over Time") +\ xlab("Year") + ylab("Number of Babies Named Brittany") +\ scale_x_continuous(breaks=list(range(1960, 2021, 10))) +\ theme_minimal()
QUESTION|TASK 3
Mary, Martha, Peter, and Paul are all Christian names. From 1920 - 2000, compare the name usage of each of the four names in a single chart. What trends do you notice?
From 1920 to 2000, the name Mary started very strong but steadily declined. Martha followed a similar pattern, though it was never as common. Peter and Paul both saw moderate popularity, peaking mid-century and then declining. This chart shows a general downward trend for all four names, suggesting that traditional Christian names declined in favor of newer or more modern naming trends.
Think of a unique name from a famous movie. Plot the usage of that name and see how changes line up with the movie release. Does it look like the movie had an effect on usage?
I chose the name Elsa from Disney’s Frozen (released in 2013). There is a noticeable spike in the name’s popularity following the release of the movie, especially around 2014. This suggests a direct cultural influence of the movie on naming trends, as parents may have been inspired by the strong and memorable character.
Show the code
elsa_df = df[df['name'] =='Elsa']ggplot() +\ geom_line(data=elsa_df, mapping=aes(x='year', y='n'), color='#87CEEB', size=1.2) +\ geom_vline(xintercept=2013, color='red', linetype='dashed') +\ ggtitle("Impact of Frozen (2013) on the Name 'Elsa'") +\ xlab("Year") + ylab("Number of Babies Named Elsa") +\ scale_x_continuous(breaks=list(range(1960, 2021, 10))) +\ theme_minimal()
STRETCH QUESTION|TASK 1
Reproduce the chart Elliot using the data from the names_year.csv file.
The name Elliot has steadily increased in popularity since the 1980s. There is a clear rise that could be associated with cultural references, including E.T. the Extra-Terrestrial (1982), where the main character was named Elliot.